Stable on-line parameter identification algorithms for systems with non-parametric uncertainties and disturbances
作者:
FU-MING LEE,
I-KONG FONG,
LI-CHEN FU,
期刊:
International Journal of Control
(Taylor Available online 1996)
卷期:
Volume 65,
issue 2
页码: 329-345
ISSN:0020-7179
年代: 1996
DOI:10.1080/00207179608921700
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
An on-line parameter identification problem is formulated for linear time-invariant continuous-time systems with bounded input/output disturbances as well as non-parametric uncertainties characterized either by H2or H∞norms. Based on the formulation, a switching type gradient algorithm is proposed to estimate the parameters of the system from the available input-output data. In spite of the existence of non-parametric uncertainties and disturbances, this on-line algorithm guarantees that the estimation error is monotonically decreasing with respect to time, and the parameter estimate is convergent to a steady-state value under a mild condition. Furthermore, the algorithm is stable in the sense that the estimation error will converge to zero as both non-parametric uncertainties and disturbances gradually diminish. To evaluate the accuracy of the identified parameters, an upper bound on the estimation error is given.
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